Neural Descent for Visual 3D Human Pose and Shape

16 Aug 2020Andrei ZanfirEduard Gabriel BazavanMihai ZanfirWilliam T. FreemanRahul SukthankarCristian Sminchisescu

We present deep neural network methodology to reconstruct the 3d pose and shape of people, given an input RGB image. We rely on a recently introduced, expressivefull body statistical 3d human model, GHUM, trained end-to-end, and learn to reconstruct its pose and shape state in a self-supervised regime... (read more)

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